Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine
Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/8/1469 |
| _version_ | 1797538127236038656 |
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| author | Jiwei Li David E. Knapp Mitchell Lyons Chris Roelfsema Stuart Phinn Steven R. Schill Gregory P. Asner |
| author_facet | Jiwei Li David E. Knapp Mitchell Lyons Chris Roelfsema Stuart Phinn Steven R. Schill Gregory P. Asner |
| author_sort | Jiwei Li |
| collection | DOAJ |
| description | Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs). |
| first_indexed | 2024-03-10T12:27:01Z |
| format | Article |
| id | doaj.art-6ae04030ad514fd0a85ae966975279c2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:27:01Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6ae04030ad514fd0a85ae966975279c22023-11-21T14:58:29ZengMDPI AGRemote Sensing2072-42922021-04-01138146910.3390/rs13081469Automated Global Shallow Water Bathymetry Mapping Using Google Earth EngineJiwei Li0David E. Knapp1Mitchell Lyons2Chris Roelfsema3Stuart Phinn4Steven R. Schill5Gregory P. Asner6Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USACenter for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USARemote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane QLD 4072, AustraliaRemote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane QLD 4072, AustraliaRemote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane QLD 4072, AustraliaThe Nature Conservancy, Caribbean Division, Coral Gables, FL 33134, USACenter for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USAGlobal shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).https://www.mdpi.com/2072-4292/13/8/1469Allen Coral AtlasGoogle Earth EngineSentinel-2bathymetrycoral reefseagrass |
| spellingShingle | Jiwei Li David E. Knapp Mitchell Lyons Chris Roelfsema Stuart Phinn Steven R. Schill Gregory P. Asner Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine Remote Sensing Allen Coral Atlas Google Earth Engine Sentinel-2 bathymetry coral reef seagrass |
| title | Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine |
| title_full | Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine |
| title_fullStr | Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine |
| title_full_unstemmed | Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine |
| title_short | Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine |
| title_sort | automated global shallow water bathymetry mapping using google earth engine |
| topic | Allen Coral Atlas Google Earth Engine Sentinel-2 bathymetry coral reef seagrass |
| url | https://www.mdpi.com/2072-4292/13/8/1469 |
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